Document Type : Research paper


1 Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India

2 Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal

3 Deputy Director, National Power Training Institute, Faridabad, Haryana, India


The microgrid (μG) is an integration of distributed generation and local loads with energy storage system. Cost minimization is one of the main objectives in modern power systems.Economic dispatch(ED) is a fundamental problem related to μG and the conventional grid. Economic dispatch(ED) provides the optimal output of generators in order to reduce the total operating cost. Emission dispatch (EMD) is one of the other major problems associated with CG. The emission dispatch (EMD) solution provides the optimal generator operation to reduce harmful pollutants for a specific load demand. Multi-objective economic emission dispatch (MEED) provides a compromise between ED and EMD. In this paper, two test systems have been proposed. Test system one consists of Six CG. Static ED, EMD, and MOEED analysis has been provided for test system one. Test system two consists of four CG, One wind turbine generator (WTG), and one photovoltaic module (PVM).This paper intends to provide sensitivity analysis and uncertainty regarding the curtailment cost of RES. CPLEX solver in GAMS has been proposed to optimize the three fundamental problems. Comparative study and sensitivity analysis show optimal results, and the GAMS solver provides a more comprehensive framework. Reduction in cost due to uncertainty in ED is 9.58% as compared to 9.7% for test system two. The cost has been reduced in MEED by 9.33% as compared to 9.46%. MEED comparison shows the increment in cost of 2.66 %, but the emission is reduced by 18.98 % for test system two.


Main Subjects

  1. Masoudi and H. Abdi, “Multi-objective stochastic programming in microgrids considering environmental emissions,” J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 141–151, 2020.
  2. Rai, A. Shrivastava, and K. Jana, “A cost-emissionbased multi-objective dynamic economic dispatch considering solar-wind curtailment cost,” IETE J. Res., pp. 1–7, 2021.
  3. Soroudi, Power system optimization modeling in GAMS, vol. 78. Springer, 2017.
  4. Khatsu, A. Srivastava, and D.K. Das, “Solving combined economic emission dispatch for microgrid using time varying phasor particle swarm optimization,” in 2020 6th Int. Conf. Adv. Comput. Commun. Syst. (ICACCS), pp. 411–415, IEEE, 2020.
  5. Dey, S.K. Roy, and B. Bhattacharyya, “Solving multiobjective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms,” Eng. Sci. Technol. an Int. J., vol. 22, no. 1, pp. 55–66, 2019.
  6. H. Aghdam, N.T. Kalantari, and B. Mohammadi-Ivatloo, “A stochastic optimal scheduling of multi-microgrid systems considering emissions: A chance constrained model,” J. Clean. Prod., vol. 275, p. 122965, 2020.
  7. Zhao, H. Qiu, R. Qin, X. Zhang, W. Gu, and Wang, “Robust optimal dispatch of ac/dc hybrid microgridsconsidering generation and load uncertainties and energy storage loss,” IEEE Trans. Power Syst., vol. 33, no. 6, pp. 5945–5957, 2018.
  8. Li, P. Wang, H.B. Gooi, J. Ye, and L. Wu, “Multi-objective optimal dispatch of microgrid under uncertainties via interval optimization,” IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 2046–2058, 2017.
  9. P. Hilbers, D.J. Brayshaw, and A. Gandy, “Efficient quantification of the impact of demand and weather uncertainty in power system models,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 1771–1779, 2020.
  10. Farrokhifar, F.H. Aghdam, A. Alahyari, A. Monavari, and A. Safari, “Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic milp model,” Electr. Power Syst. Res., vol. 187, p. 106483, 2020.
  11. H. Aghdam, N.T. Kalantari, and B. Mohammadi-Ivatloo, “A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements,” J. Energy Storage, vol. 29, p. 101416, 2020.
  12. Kumar, S. Dahiya, and K. Singh Parmar, “Multi-objective economic emission dispatch optimization strategy considering battery energy storage system in islanded microgrid,” J. Oper. Autom. Power Eng., 2023.
  13. Liu, Y. Chen, R. Zhuo, and H. Jia, “Energy storage capacity optimization for autonomy microgrid considering chp and ev scheduling,” Appl. Energy, vol. 210, pp. 1113–1125, 2018.
  14. Yousif, Q. Ai, Y. Gao, W.A. Wattoo, Z. Jiang, and R. Hao, “An optimal dispatch strategy for distributed microgrids using pso,” CSEE J. Power Energy Syst., vol. 6, no. 3, pp. 724–734, 2019.
  15. M. Vallem and A. Kumar, “Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response,” Int. Trans. Electr. Energy Syst., vol. 30, no. 5, p. e12328, 2020.
  16. Kumar, S. Dahiya, and K.P.S. Parmar, “Cost based optimal dynamic economic dispatch with wind integration,” in 2020 IEEE 9th Power India International Conference (PIICON), pp. 1–5, IEEE, 2020.
  17. Dou, X. Zhou, T. Zhang, and S. Xu, “Economic optimization dispatching strategy of microgrid for promoting photoelectric consumption considering cogeneration and demand response,” . Mod. Power Syst. Clean Energy, vol. 8, no. 3, pp. 557–563, 2020.
  18. Zhang, F. Meng, R. Wang, B. Kazemtabrizi, and J. Shi, “Uncertainty-resistant stochastic mpc approach for optimal operation of chp microgrid,” Energy, vol. 179, pp. 1265–1278, 2019.
  19. Chinnadurrai and T.A.A. Victoire, “Dynamic economic emission dispatch considering wind uncertainty using nondominated sorting crisscross optimization,” IEEE Access, vol. 8, pp. 94678–94696, 2020.
  20. Hasankhani and S.M. Hakimi, “Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market,” Energy, vol. 219, p. 119668, 2021.
  21. A. Wolak, “Market design in an intermittent renewable future: cost recovery with zero-marginal-cost resources,” IEEE Power Electron. Mag., vol. 19, no. 1, pp. 29–40, 2021.
  22. M. Mohiuddin and J. Qi, “Optimal distributed control of ac microgrids with coordinated voltage regulation and reactive power sharing,” IEEE Trans. Smart Grid, 2022.
  23. Hennane, A. Berdai, S. Pierfederici, F. Meibody-Tabar, and J.-P. Martin, “Novel non-linear control for synchronization and power sharing in islanded and grid-connected mesh microgrids,” Electr. Power Syst. Res., vol. 208, p. 107869, 2022.
  24. Kong, L. Bai, Q. Hu, F. Li, and C. Wang, “Day-ahead optimal scheduling method for grid-connected microgrid based on energy storage control strategy,” J. Mod. Power Syst. Clean Energy, vol. 4, no. 4, pp. 648–658, 2016.
  25. Khodaei, “Resiliency-oriented microgrid optimal scheduling,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1584–1591, 2014.
  26. S. Kumar, A.K. Rastogi, B. Rajani, A. Mehbodniya, K. Karunanithi, and D. Devarapalli, “Optimal solution to economic load dispatch by modified jaya algorithm,” in 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 348–352, IEEE, 2021.
  27. Agbonaye, P. Keatley, Y. Huang, O.F. Odiase, and N. Hewitt, “Value of demand flexibility for managing wind energy constraint and curtailment,” Renew. Energy, 2022.
  28. K. Jadoun, G.R. Prashanth, S.S. Joshi, K. Narayanan, H. Malik, and F.P.G. Márquez, “Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled whale optimization algorithm,” Appl. Energy, vol. 315, p. 119033, 2022.
  29. Nemati, M. Braun, and S. Tenbohlen, “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming,” Appl. energy, vol. 210, pp. 944–963, 2018.
  30. Zhu, Y. Jin, W. Zhu, D.-K. Lee, and N. Bohlooli, “Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response,” Int. J. Hydrog. Energy, 2023.
  31. Parvin, H. Yousefi, and Y. Noorollahi, “Techno-economic optimization of a renewable micro grid using multi-objective particle swarm optimization algorithm,” Energy Convers. Manag., vol. 277, p. 116639, 2023.
  32. Singh, M. Pandit, and L. Srivastava, “Multi-objective optimal sizing of hybrid micro-grid system using an integrated intelligent technique,” Energy, p. 126756, 2023.
  33. Yang, Y. Cui, and J. Wang, “Multi-objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output,” Int. J. Electr. Power Energy Syst., vol. 144, p. 108619, 2023.
  34. Vaish, A.K. Tiwari, and K.M. Siddiqui, “Optimization of micro grid with distributed energy resources using physics based meta heuristic techniques,” IET Renew. Power Gener., 2023.
  35. Li, M. Yang, Y. Zhang, J. Li, and J. Lu, “Micro-grid day-ahead stochastic optimal dispatch considering multiple demand response and electric vehicles,” Energies, vol. 16, no. 8, p. 3356, 2023.
  36. H. Aghdam and N.T. Kalantari, “Energy management requirements for microgrids,” Microgrid architectures, control and Protection methods, pp. 233–253, 2020.
  37. H. Aghdam, M.S. Javadi, and J.P. Catalão, “Optimal stochastic operation of technical virtual power plants in reconfigurable distribution networks considering contingencies,” Int. J. Electr. Power Energy Syst., vol. 147, p. 108799, 2023.
  38. Azimi and A. Salami, “Optimal operation of integrated energy systems considering demand response program,” J. Oper. Autom. Power Eng., vol. 9, no. 1, pp. 60–67, 2021.
  39. Xia and X. Wu, “A hybrid multi-objective optimization algorithm for economic emission dispatch considering wind power uncertainty,” Iran. J. Sci. Technol. - Trans. Electr. Eng., vol. 45, pp. 1277–1293, 2021.
  40. Hou, Q. Wang, Z. Xiao, M. Xue, Y. Wu, X. Deng, and C. Xie, “Data-driven economic dispatch for islanded micro-grid considering uncertainty and demand response,” Int. J. Electr. Power Energy Syst., vol. 136, p. 107623, 2022.
  41. Sun, J. Fu, L. Wei, and A. Li, “Multi-objective optimal dispatching for a grid-connected micro-grid considering wind power forecasting probability,” IEEE Access, vol. 8, pp. 46981–46997, 2020.
  42. P. Musau, N.A. Odero, and C.W. Wekesa, “Multi objective dynamic economic emission dispatch with renewable energy and emissions,” in 2016 IEEE PES PowerAfrica, pp. 274–279, IEEE, 2016.
  43. Behnamfar and M. Abasi, “Uncertainty management in short-term self-scheduling unit commitment using harris hawks optimization algorithm,” J. Oper. Autom. Power Eng., 2023.
  44. Bird, D. Lew, M. Milligan, E.M. Carlini, A. Estanqueiro, Flynn, E. Gomez-Lazaro, H. Holttinen, N. Menemenlis,A. Orths, et al., “Wind and solar energy curtailment: A review of international experience,” Renew. Sustain. Energy Rev., vol. 65, pp. 577–586, 2016.
  45. Dey, B. Bhattacharyya, and F.P.G. Márquez, “A hybrid optimization-based approach to solve environment constrained economic dispatch problem on microgrid system,” J. Clean. Prod., vol. 307, p. 127196, 2021.
  46. Taheri, G. Aghajani, and M. Sedaghat, “Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the pareto criterion and fuzzy logic,” Int. J. Energy Environ. Eng., vol. 8, no. 2, pp. 99–107, 2017.